Text Classification
Transformers
Safetensors
English
bert
MiniLM
MiniLM-L12
Intent-Matching
Intent
text-embeddings-inference
Instructions to use Kami0867/MiniLM-L12-Intent-Matching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kami0867/MiniLM-L12-Intent-Matching with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kami0867/MiniLM-L12-Intent-Matching")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kami0867/MiniLM-L12-Intent-Matching") model = AutoModelForSequenceClassification.from_pretrained("Kami0867/MiniLM-L12-Intent-Matching", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 190 Bytes
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license: mit
language:
- en
metrics:
- f1
- accuracy
base_model:
- microsoft/MiniLM-L12-H384-uncased
library_name: transformers
tags:
- MiniLM
- MiniLM-L12
- Intent-Matching
- Intent
--- |